SHARK TANK INDIA EDA¶

This is an Exploratory Data Analysis on the popular TV show Shark Tank India. I got the dataset from kaggle. The dataset consits of information such as the portfolio of different sharks, states from where the pitchers have come, gross margin and valuation offered to the startups.

In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
from babel.numbers import format_currency
from wordcloud import WordCloud, STOPWORDS
import plotly.express as px
import plotly.io as pio
pio.templates.default = "plotly_dark"
pio.renderers.default = 'notebook'
In [2]:
shark_tank = pd.read_csv("C:/Users/CHIRA/Downloads/Shark Tank India.csv")
shark_tank.sample(10)
Out[2]:
Season Number Episode Number Episode Title Pitch Number Startup Name Industry Business Description Company Website Number of Presenters Male Presenters ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Ghazal Investment Amount Ghazal Investment Equity Ghazal Debt Amount Number of sharks in deal
17 1 6 New Week, New Ideas 18 Hecoll Beauty/Fashion Pollution Resistant Fabric - Healthy Cover For... https://hecoll.com/ 1 NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
81 1 26 Revolutionary Ideas 82 Isak Fragrances Beauty/Fashion Perfumes Fragrances https://isakfragrances.com/ 1 NaN ... NaN NaN NaN 50.0 50.0 NaN 0.0 0.0 NaN 1.0
113 1 34 Scaling Ambitions 114 On2Cook Food Fastest Cooking Device https://on2cook.com/ 1 1.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
84 1 27 Investing In The Future Of India 85 Theka Coffee Food Coffee Products NaN 1 1.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
13 1 5 Hunt For Interesting Business 14 Hungry Head Food Restaurant serving 80 types of Maggi NaN 2 2.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
30 1 11 Investment Paane Ka Sapna 31 Gopal's 56 Food Fiber Ice Cream https://www.gopals56.in/ 1 1.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
69 1 22 Turning Ideas Into Businesses 70 Moonshine Food Meads https://www.moonshinemeadery.com/ 2 2.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
46 1 16 Winning The Sharks Trust 47 Flying Fur Animal/Pets Dog Hygiene https://flyingfur.in/ 3 2.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
49 1 17 A Wave Of Startups 50 Find Your Kicks India Beauty/Fashion Sneaker Resale https://findyourkicksindia.co/ 3 3.0 ... 10.0 5.0 NaN 10.0 5.0 NaN NaN NaN NaN 5.0
109 1 33 Life-Changing Ideas 110 Proxgy Technology VR https://www.proxgy.com/ 2 2.0 ... 0.0 0.0 NaN 50.0 5.0 NaN NaN NaN NaN 2.0

10 rows × 50 columns

In [3]:
shark_tank.shape
Out[3]:
(121, 50)
In [4]:
shark_tank.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 121 entries, 0 to 120
Data columns (total 50 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   Season Number              121 non-null    int64  
 1   Episode Number             121 non-null    int64  
 2   Episode Title              121 non-null    object 
 3   Pitch Number               121 non-null    int64  
 4   Startup Name               121 non-null    object 
 5   Industry                   121 non-null    object 
 6   Business Description       121 non-null    object 
 7   Company Website            112 non-null    object 
 8   Number of Presenters       121 non-null    int64  
 9   Male Presenters            102 non-null    float64
 10  Female Presenters          62 non-null     float64
 11  Couple Presenters          120 non-null    float64
 12  Pitchers Average Age       121 non-null    object 
 13  Started in                 95 non-null     float64
 14  Pitchers City              114 non-null    object 
 15  Pitchers State             116 non-null    object 
 16  Yearly Revenue             52 non-null     float64
 17  Monthly Sales              65 non-null     float64
 18  Gross Margin               35 non-null     float64
 19  Original Ask Amount        121 non-null    float64
 20  Original Ask Equity        121 non-null    float64
 21  Valuation Requested        121 non-null    int64  
 22  Received Offer             121 non-null    int64  
 23  Accepted Offer             88 non-null     float64
 24  Total Deal Amount          67 non-null     float64
 25  Total Deal Equity          67 non-null     float64
 26  Total Deal Debt            9 non-null      float64
 27  Valuation Offered          67 non-null     float64
 28  Ashneer Investment Amount  54 non-null     float64
 29  Ashneer Investment Equity  54 non-null     float64
 30  Ashneer Debt Amount        2 non-null      float64
 31  Namita Investment Amount   62 non-null     float64
 32  Namita Investment Equity   62 non-null     float64
 33  Namita Debt Amount         1 non-null      float64
 34  Anupam Investment Amount   67 non-null     float64
 35  Anupam Investment Equity   67 non-null     float64
 36  Anupam Debt Amount         1 non-null      float64
 37  Vineeta Investment Amount  34 non-null     float64
 38  Vineeta Investment Equity  34 non-null     float64
 39  Vineeta Debt Amount        1 non-null      float64
 40  Aman Investment Amount     56 non-null     float64
 41  Aman Investment Equity     56 non-null     float64
 42  Aman Debt Amount           1 non-null      float64
 43  Peyush Investment Amount   53 non-null     float64
 44  Peyush Investment Equity   53 non-null     float64
 45  Peyush Debt Amount         5 non-null      float64
 46  Ghazal Investment Amount   13 non-null     float64
 47  Ghazal Investment Equity   13 non-null     float64
 48  Ghazal Debt Amount         0 non-null      float64
 49  Number of sharks in deal   67 non-null     float64
dtypes: float64(36), int64(6), object(8)
memory usage: 47.4+ KB
In [5]:
shark_tank.describe()
Out[5]:
Season Number Episode Number Pitch Number Number of Presenters Male Presenters Female Presenters Couple Presenters Started in Yearly Revenue Monthly Sales ... Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Ghazal Investment Amount Ghazal Investment Equity Ghazal Debt Amount Number of sharks in deal
count 121.0 121.000000 121.000000 121.000000 102.000000 62.000000 120.000000 95.000000 52.000000 6.500000e+01 ... 56.000000 56.000000 1.0 53.000000 53.000000 5.000000 13.000000 13.000000 0.0 67.000000
mean 1.0 19.305785 61.000000 2.082645 1.735294 1.209677 0.208333 2018.052632 405.134615 1.455049e+06 ... 15.973036 2.932964 50.0 14.899061 5.996981 23.400000 9.999250 3.592308 NaN 2.223881
std 0.0 10.375326 35.073732 0.927243 0.974186 0.483739 0.407819 2.481285 1055.349288 3.167937e+06 ... 22.947765 5.810317 NaN 21.192558 13.631118 2.302173 12.378205 5.322352 NaN 1.165422
min 1.0 1.000000 1.000000 1.000000 1.000000 1.000000 0.000000 2005.000000 0.000000 0.000000e+00 ... 0.000000 0.000000 50.0 0.000000 0.000000 20.000000 0.000000 0.000000 NaN 1.000000
25% 1.0 11.000000 31.000000 1.000000 1.000000 1.000000 0.000000 2017.000000 53.750000 2.000000e+05 ... 0.000000 0.000000 50.0 0.000000 0.000000 22.000000 0.000000 0.000000 NaN 1.000000
50% 1.0 19.000000 61.000000 2.000000 1.000000 1.000000 0.000000 2019.000000 112.500000 5.500000e+05 ... 3.500008 0.875000 50.0 8.330000 1.000000 25.000000 0.000253 1.000000 NaN 2.000000
75% 1.0 28.000000 91.000000 3.000000 2.000000 1.000000 0.000000 2020.000000 255.500000 1.600000e+06 ... 25.000000 4.250000 50.0 25.000000 5.000000 25.000000 20.000000 5.000000 NaN 3.000000
max 1.0 36.000000 121.000000 6.000000 6.000000 3.000000 1.000000 2022.000000 7200.000000 2.000000e+07 ... 100.000000 40.000000 50.0 100.000000 75.000000 25.000000 33.330000 17.500000 NaN 5.000000

8 rows × 42 columns

In [6]:
shark_tank.columns
Out[6]:
Index(['Season Number', 'Episode Number', 'Episode Title', 'Pitch Number',
       'Startup Name', 'Industry', 'Business Description', 'Company Website',
       'Number of Presenters', 'Male Presenters', 'Female Presenters',
       'Couple Presenters', 'Pitchers Average Age', 'Started in',
       'Pitchers City', 'Pitchers State', 'Yearly Revenue', 'Monthly Sales',
       'Gross Margin', 'Original Ask Amount', 'Original Ask Equity',
       'Valuation Requested', 'Received Offer', 'Accepted Offer',
       'Total Deal Amount', 'Total Deal Equity', 'Total Deal Debt',
       'Valuation Offered', 'Ashneer Investment Amount',
       'Ashneer Investment Equity', 'Ashneer Debt Amount',
       'Namita Investment Amount', 'Namita Investment Equity',
       'Namita Debt Amount', 'Anupam Investment Amount',
       'Anupam Investment Equity', 'Anupam Debt Amount',
       'Vineeta Investment Amount', 'Vineeta Investment Equity',
       'Vineeta Debt Amount', 'Aman Investment Amount',
       'Aman Investment Equity', 'Aman Debt Amount',
       'Peyush Investment Amount', 'Peyush Investment Equity',
       'Peyush Debt Amount', 'Ghazal Investment Amount',
       'Ghazal Investment Equity', 'Ghazal Debt Amount',
       'Number of sharks in deal'],
      dtype='object')
In [7]:
shark_tank.isnull().sum()
Out[7]:
Season Number                  0
Episode Number                 0
Episode Title                  0
Pitch Number                   0
Startup Name                   0
Industry                       0
Business Description           0
Company Website                9
Number of Presenters           0
Male Presenters               19
Female Presenters             59
Couple Presenters              1
Pitchers Average Age           0
Started in                    26
Pitchers City                  7
Pitchers State                 5
Yearly Revenue                69
Monthly Sales                 56
Gross Margin                  86
Original Ask Amount            0
Original Ask Equity            0
Valuation Requested            0
Received Offer                 0
Accepted Offer                33
Total Deal Amount             54
Total Deal Equity             54
Total Deal Debt              112
Valuation Offered             54
Ashneer Investment Amount     67
Ashneer Investment Equity     67
Ashneer Debt Amount          119
Namita Investment Amount      59
Namita Investment Equity      59
Namita Debt Amount           120
Anupam Investment Amount      54
Anupam Investment Equity      54
Anupam Debt Amount           120
Vineeta Investment Amount     87
Vineeta Investment Equity     87
Vineeta Debt Amount          120
Aman Investment Amount        65
Aman Investment Equity        65
Aman Debt Amount             120
Peyush Investment Amount      68
Peyush Investment Equity      68
Peyush Debt Amount           116
Ghazal Investment Amount     108
Ghazal Investment Equity     108
Ghazal Debt Amount           121
Number of sharks in deal      54
dtype: int64

Not removing null values here as the null values are not the outliers here, they play an important role in the data. For example, if Ashneer's debt amount is Nan, that means he hasn't given loan to the pitchers and so on.

Correlation Matrix¶

In [8]:
shark_tank.corr(method = 'pearson').T.round(2).style.background_gradient(cmap='PuBu')
Out[8]:
  Season Number Episode Number Pitch Number Number of Presenters Male Presenters Female Presenters Couple Presenters Started in Yearly Revenue Monthly Sales Gross Margin Original Ask Amount Original Ask Equity Valuation Requested Received Offer Accepted Offer Total Deal Amount Total Deal Equity Total Deal Debt Valuation Offered Ashneer Investment Amount Ashneer Investment Equity Ashneer Debt Amount Namita Investment Amount Namita Investment Equity Namita Debt Amount Anupam Investment Amount Anupam Investment Equity Anupam Debt Amount Vineeta Investment Amount Vineeta Investment Equity Vineeta Debt Amount Aman Investment Amount Aman Investment Equity Aman Debt Amount Peyush Investment Amount Peyush Investment Equity Peyush Debt Amount Ghazal Investment Amount Ghazal Investment Equity Ghazal Debt Amount Number of sharks in deal
Season Number nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Episode Number nan 1.000000 1.000000 -0.010000 0.060000 -0.170000 -0.070000 0.100000 0.210000 -0.020000 -0.110000 -0.070000 -0.100000 -0.010000 -0.090000 -0.230000 -0.010000 -0.010000 0.140000 -0.060000 -0.030000 -0.040000 1.000000 0.060000 0.080000 nan -0.130000 -0.120000 nan -0.030000 -0.300000 nan -0.070000 -0.030000 nan 0.080000 0.010000 0.040000 -0.030000 -0.070000 nan 0.110000
Pitch Number nan 1.000000 1.000000 -0.010000 0.060000 -0.170000 -0.060000 0.100000 0.220000 -0.020000 -0.120000 -0.080000 -0.110000 -0.010000 -0.100000 -0.240000 -0.020000 -0.010000 0.150000 -0.060000 -0.020000 -0.020000 1.000000 0.050000 0.070000 nan -0.140000 -0.120000 nan -0.040000 -0.290000 nan -0.080000 -0.040000 nan 0.090000 0.010000 0.030000 -0.060000 -0.080000 nan 0.110000
Number of Presenters nan -0.010000 -0.010000 1.000000 0.770000 0.400000 0.110000 -0.010000 0.020000 -0.110000 -0.210000 -0.110000 -0.220000 -0.090000 0.090000 -0.090000 0.160000 -0.340000 0.140000 0.270000 0.040000 -0.080000 nan -0.060000 -0.110000 nan 0.190000 -0.130000 nan 0.160000 -0.220000 nan -0.010000 0.060000 nan 0.070000 -0.230000 0.460000 0.300000 -0.170000 nan 0.180000
Male Presenters nan 0.060000 0.060000 0.770000 1.000000 -0.160000 -0.360000 0.020000 0.060000 -0.160000 -0.060000 -0.080000 -0.210000 -0.050000 0.200000 0.000000 0.020000 -0.230000 0.370000 0.220000 -0.020000 -0.060000 1.000000 -0.100000 -0.220000 nan 0.250000 -0.010000 nan -0.050000 -0.240000 nan 0.020000 0.060000 nan -0.050000 -0.150000 -0.040000 0.260000 -0.090000 nan 0.120000
Female Presenters nan -0.170000 -0.170000 0.400000 -0.160000 1.000000 -0.090000 0.020000 -0.230000 -0.160000 -0.010000 -0.130000 0.090000 0.070000 -0.080000 -0.160000 -0.230000 -0.020000 nan -0.180000 -0.210000 -0.200000 nan -0.190000 -0.100000 nan 0.180000 0.090000 nan nan nan nan 0.020000 0.220000 nan -0.150000 -0.020000 nan nan nan nan 0.060000
Couple Presenters nan -0.070000 -0.060000 0.110000 -0.360000 -0.090000 1.000000 -0.090000 0.040000 0.140000 -0.280000 -0.050000 -0.060000 -0.050000 -0.100000 -0.080000 0.190000 -0.120000 0.020000 0.090000 0.130000 -0.070000 -1.000000 0.220000 0.280000 nan 0.120000 -0.030000 nan -0.030000 -0.060000 nan -0.120000 -0.130000 nan 0.080000 -0.120000 0.390000 nan nan nan 0.020000
Started in nan 0.100000 0.100000 -0.010000 0.020000 0.020000 -0.090000 1.000000 -0.040000 -0.200000 0.390000 -0.170000 -0.020000 -0.150000 0.170000 0.110000 -0.150000 -0.040000 0.110000 -0.330000 -0.040000 -0.190000 1.000000 0.020000 0.170000 nan -0.080000 0.160000 nan 0.080000 0.160000 nan -0.160000 0.140000 nan -0.200000 -0.230000 0.960000 0.480000 0.660000 nan 0.230000
Yearly Revenue nan 0.210000 0.220000 0.020000 0.060000 -0.230000 0.040000 -0.040000 1.000000 0.270000 0.230000 -0.040000 -0.200000 0.260000 0.210000 -0.360000 0.340000 0.000000 nan 0.410000 0.380000 -0.210000 nan -0.220000 -0.160000 nan -0.120000 -0.140000 nan -0.300000 -0.360000 nan 0.480000 0.480000 nan -0.090000 -0.090000 nan 0.960000 -0.510000 nan -0.270000
Monthly Sales nan -0.020000 -0.020000 -0.110000 -0.160000 -0.160000 0.140000 -0.200000 0.270000 1.000000 -0.430000 0.080000 -0.190000 0.130000 0.120000 -0.370000 0.120000 -0.490000 0.200000 0.150000 0.180000 -0.200000 nan 0.010000 -0.190000 nan -0.010000 -0.150000 nan -0.330000 -0.270000 nan -0.040000 -0.210000 nan 0.310000 -0.230000 0.930000 -0.250000 -0.160000 nan -0.140000
Gross Margin nan -0.110000 -0.120000 -0.210000 -0.060000 -0.010000 -0.280000 0.390000 0.230000 -0.430000 1.000000 -0.250000 0.270000 -0.050000 -0.010000 0.450000 -0.260000 -0.010000 nan -0.230000 -0.230000 -0.040000 nan 0.360000 0.620000 nan -0.010000 0.020000 nan 0.050000 -0.010000 nan -0.340000 -0.060000 nan -0.440000 -0.560000 nan -0.200000 0.120000 nan 0.010000
Original Ask Amount nan -0.070000 -0.080000 -0.110000 -0.080000 -0.130000 -0.050000 -0.170000 -0.040000 0.080000 -0.250000 1.000000 0.500000 0.860000 -0.150000 -0.400000 0.610000 -0.290000 0.750000 0.420000 0.260000 -0.040000 1.000000 0.340000 0.000000 nan -0.060000 -0.240000 nan 0.040000 -0.340000 nan 0.230000 -0.150000 nan 0.320000 -0.070000 0.730000 0.350000 -0.080000 nan -0.150000
Original Ask Equity nan -0.100000 -0.110000 -0.220000 -0.210000 0.090000 -0.060000 -0.020000 -0.200000 -0.190000 0.270000 0.500000 1.000000 0.220000 -0.260000 0.250000 -0.320000 0.730000 -0.530000 -0.540000 -0.210000 0.380000 -1.000000 -0.060000 0.310000 nan -0.060000 0.260000 nan 0.100000 0.480000 nan -0.260000 -0.020000 nan -0.080000 0.430000 -0.930000 0.000000 0.260000 nan 0.000000
Valuation Requested nan -0.010000 -0.010000 -0.090000 -0.050000 0.070000 -0.050000 -0.150000 0.260000 0.130000 -0.050000 0.860000 0.220000 1.000000 -0.090000 -0.380000 0.400000 -0.530000 0.920000 0.740000 0.220000 -0.120000 1.000000 0.170000 -0.170000 nan 0.060000 -0.290000 nan -0.110000 -0.350000 nan 0.120000 -0.190000 nan 0.370000 -0.210000 0.700000 -0.140000 -0.310000 nan -0.180000
Received Offer nan -0.090000 -0.100000 0.090000 0.200000 -0.080000 -0.100000 0.170000 0.210000 0.120000 -0.010000 -0.150000 -0.260000 -0.090000 1.000000 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Accepted Offer nan -0.230000 -0.240000 -0.090000 0.000000 -0.160000 -0.080000 0.110000 -0.360000 -0.370000 0.450000 -0.400000 0.250000 -0.380000 nan 1.000000 nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Total Deal Amount nan -0.010000 -0.020000 0.160000 0.020000 -0.230000 0.190000 -0.150000 0.340000 0.120000 -0.260000 0.610000 -0.320000 0.400000 nan nan 1.000000 -0.230000 -0.270000 0.500000 0.440000 0.060000 -1.000000 0.350000 -0.010000 nan 0.140000 -0.180000 nan 0.260000 -0.280000 nan 0.400000 0.140000 nan 0.430000 -0.190000 0.810000 0.510000 0.050000 nan 0.190000
Total Deal Equity nan -0.010000 -0.010000 -0.340000 -0.230000 -0.020000 -0.120000 -0.040000 0.000000 -0.490000 -0.010000 -0.290000 0.730000 -0.530000 nan nan -0.230000 1.000000 -0.490000 -0.520000 -0.210000 0.290000 -1.000000 -0.160000 0.080000 nan -0.040000 0.260000 nan 0.040000 0.360000 nan -0.150000 0.220000 nan 0.050000 0.770000 -0.690000 -0.000000 0.270000 nan -0.100000
Total Deal Debt nan 0.140000 0.150000 0.140000 0.370000 nan 0.020000 0.110000 nan 0.200000 nan 0.750000 -0.530000 0.920000 nan nan -0.270000 -0.490000 1.000000 -0.080000 -0.130000 0.350000 1.000000 0.160000 0.160000 nan -0.130000 -0.130000 nan -0.170000 -0.170000 nan 0.140000 0.140000 nan -0.490000 -0.500000 0.540000 nan nan nan 0.020000
Valuation Offered nan -0.060000 -0.060000 0.270000 0.220000 -0.180000 0.090000 -0.330000 0.410000 0.150000 -0.230000 0.420000 -0.540000 0.740000 nan nan 0.500000 -0.520000 -0.080000 1.000000 0.190000 -0.120000 -1.000000 0.090000 -0.170000 nan 0.250000 -0.260000 nan -0.260000 -0.370000 nan 0.360000 -0.150000 nan 0.290000 -0.260000 0.660000 -0.090000 -0.310000 nan -0.080000
Ashneer Investment Amount nan -0.030000 -0.020000 0.040000 -0.020000 -0.210000 0.130000 -0.040000 0.380000 0.180000 -0.230000 0.260000 -0.210000 0.220000 nan nan 0.440000 -0.210000 -0.130000 0.190000 1.000000 0.420000 -1.000000 -0.110000 -0.180000 nan -0.120000 -0.230000 nan -0.030000 -0.130000 nan -0.100000 -0.160000 nan 0.250000 -0.150000 nan nan nan nan 0.220000
Ashneer Investment Equity nan -0.040000 -0.020000 -0.080000 -0.060000 -0.200000 -0.070000 -0.190000 -0.210000 -0.200000 -0.040000 -0.040000 0.380000 -0.120000 nan nan 0.060000 0.290000 0.350000 -0.120000 0.420000 1.000000 -1.000000 -0.130000 -0.130000 nan -0.130000 -0.120000 nan 0.250000 0.610000 nan -0.160000 -0.090000 nan 0.040000 -0.170000 nan -1.000000 -1.000000 nan 0.250000
Ashneer Debt Amount nan 1.000000 1.000000 nan 1.000000 nan -1.000000 1.000000 nan nan nan 1.000000 -1.000000 1.000000 nan nan -1.000000 -1.000000 1.000000 -1.000000 -1.000000 -1.000000 1.000000 nan nan nan -1.000000 -1.000000 nan nan nan nan nan nan nan nan nan nan nan nan nan -1.000000
Namita Investment Amount nan 0.060000 0.050000 -0.060000 -0.100000 -0.190000 0.220000 0.020000 -0.220000 0.010000 0.360000 0.340000 -0.060000 0.170000 nan nan 0.350000 -0.160000 0.160000 0.090000 -0.110000 -0.130000 nan 1.000000 0.670000 nan -0.190000 -0.270000 nan 0.040000 -0.250000 nan -0.080000 -0.110000 nan -0.140000 -0.240000 0.330000 -0.030000 -0.150000 nan -0.010000
Namita Investment Equity nan 0.080000 0.070000 -0.110000 -0.220000 -0.100000 0.280000 0.170000 -0.160000 -0.190000 0.620000 0.000000 0.310000 -0.170000 nan nan -0.010000 0.080000 0.160000 -0.170000 -0.180000 -0.130000 nan 0.670000 1.000000 nan -0.230000 -0.220000 nan -0.050000 -0.160000 nan -0.160000 -0.050000 nan -0.250000 -0.210000 0.330000 0.230000 0.100000 nan -0.060000
Namita Debt Amount nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Anupam Investment Amount nan -0.130000 -0.140000 0.190000 0.250000 0.180000 0.120000 -0.080000 -0.120000 -0.010000 -0.010000 -0.060000 -0.060000 0.060000 nan nan 0.140000 -0.040000 -0.130000 0.250000 -0.120000 -0.130000 -1.000000 -0.190000 -0.230000 nan 1.000000 0.680000 nan -0.090000 -0.050000 nan -0.130000 -0.160000 nan -0.080000 -0.150000 nan -0.010000 0.090000 nan 0.150000
Anupam Investment Equity nan -0.120000 -0.120000 -0.130000 -0.010000 0.090000 -0.030000 0.160000 -0.140000 -0.150000 0.020000 -0.240000 0.260000 -0.290000 nan nan -0.180000 0.260000 -0.130000 -0.260000 -0.230000 -0.120000 -1.000000 -0.270000 -0.220000 nan 0.680000 1.000000 nan 0.010000 0.240000 nan -0.260000 -0.100000 nan -0.160000 -0.080000 nan 0.040000 0.400000 nan 0.030000
Anupam Debt Amount nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Vineeta Investment Amount nan -0.030000 -0.040000 0.160000 -0.050000 nan -0.030000 0.080000 -0.300000 -0.330000 0.050000 0.040000 0.100000 -0.110000 nan nan 0.260000 0.040000 -0.170000 -0.260000 -0.030000 0.250000 nan 0.040000 -0.050000 nan -0.090000 0.010000 nan 1.000000 0.550000 nan -0.320000 -0.080000 nan -0.150000 -0.280000 nan 0.800000 0.230000 nan 0.430000
Vineeta Investment Equity nan -0.300000 -0.290000 -0.220000 -0.240000 nan -0.060000 0.160000 -0.360000 -0.270000 -0.010000 -0.340000 0.480000 -0.350000 nan nan -0.280000 0.360000 -0.170000 -0.370000 -0.130000 0.610000 nan -0.250000 -0.160000 nan -0.050000 0.240000 nan 0.550000 1.000000 nan -0.440000 -0.250000 nan -0.270000 -0.270000 nan 0.420000 0.310000 nan 0.130000
Vineeta Debt Amount nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Aman Investment Amount nan -0.070000 -0.080000 -0.010000 0.020000 0.020000 -0.120000 -0.160000 0.480000 -0.040000 -0.340000 0.230000 -0.260000 0.120000 nan nan 0.400000 -0.150000 0.140000 0.360000 -0.100000 -0.160000 nan -0.080000 -0.160000 nan -0.130000 -0.260000 nan -0.320000 -0.440000 nan 1.000000 0.650000 nan -0.340000 -0.210000 nan -1.000000 -1.000000 nan -0.130000
Aman Investment Equity nan -0.030000 -0.040000 0.060000 0.060000 0.220000 -0.130000 0.140000 0.480000 -0.210000 -0.060000 -0.150000 -0.020000 -0.190000 nan nan 0.140000 0.220000 0.140000 -0.150000 -0.160000 -0.090000 nan -0.110000 -0.050000 nan -0.160000 -0.100000 nan -0.080000 -0.250000 nan 0.650000 1.000000 nan -0.270000 -0.140000 nan -1.000000 -1.000000 nan -0.010000
Aman Debt Amount nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Peyush Investment Amount nan 0.080000 0.090000 0.070000 -0.050000 -0.150000 0.080000 -0.200000 -0.090000 0.310000 -0.440000 0.320000 -0.080000 0.370000 nan nan 0.430000 0.050000 -0.490000 0.290000 0.250000 0.040000 nan -0.140000 -0.250000 nan -0.080000 -0.160000 nan -0.150000 -0.270000 nan -0.340000 -0.270000 nan 1.000000 0.340000 0.770000 -0.120000 -0.250000 nan -0.090000
Peyush Investment Equity nan 0.010000 0.010000 -0.230000 -0.150000 -0.020000 -0.120000 -0.230000 -0.090000 -0.230000 -0.560000 -0.070000 0.430000 -0.210000 nan nan -0.190000 0.770000 -0.500000 -0.260000 -0.150000 -0.170000 nan -0.240000 -0.210000 nan -0.150000 -0.080000 nan -0.280000 -0.270000 nan -0.210000 -0.140000 nan 0.340000 1.000000 -0.690000 -0.310000 -0.260000 nan -0.270000
Peyush Debt Amount nan 0.040000 0.030000 0.460000 -0.040000 nan 0.390000 0.960000 nan 0.930000 nan 0.730000 -0.930000 0.700000 nan nan 0.810000 -0.690000 0.540000 0.660000 nan nan nan 0.330000 0.330000 nan nan nan nan nan nan nan nan nan nan 0.770000 -0.690000 1.000000 nan nan nan 0.390000
Ghazal Investment Amount nan -0.030000 -0.060000 0.300000 0.260000 nan nan 0.480000 0.960000 -0.250000 -0.200000 0.350000 0.000000 -0.140000 nan nan 0.510000 -0.000000 nan -0.090000 nan -1.000000 nan -0.030000 0.230000 nan -0.010000 0.040000 nan 0.800000 0.420000 nan -1.000000 -1.000000 nan -0.120000 -0.310000 nan 1.000000 0.700000 nan 0.390000
Ghazal Investment Equity nan -0.070000 -0.080000 -0.170000 -0.090000 nan nan 0.660000 -0.510000 -0.160000 0.120000 -0.080000 0.260000 -0.310000 nan nan 0.050000 0.270000 nan -0.310000 nan -1.000000 nan -0.150000 0.100000 nan 0.090000 0.400000 nan 0.230000 0.310000 nan -1.000000 -1.000000 nan -0.250000 -0.260000 nan 0.700000 1.000000 nan 0.130000
Ghazal Debt Amount nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan
Number of sharks in deal nan 0.110000 0.110000 0.180000 0.120000 0.060000 0.020000 0.230000 -0.270000 -0.140000 0.010000 -0.150000 0.000000 -0.180000 nan nan 0.190000 -0.100000 0.020000 -0.080000 0.220000 0.250000 -1.000000 -0.010000 -0.060000 nan 0.150000 0.030000 nan 0.430000 0.130000 nan -0.130000 -0.010000 nan -0.090000 -0.270000 0.390000 0.390000 0.130000 nan 1.000000

Shark Tank India - Season 1¶

In [9]:
for col in shark_tank.columns:
    print("Number of unique values in", col, "-", shark_tank[col].nunique())

Number of unique values in Season Number - 1
Number of unique values in Episode Number - 36
Number of unique values in Episode Title - 36
Number of unique values in Pitch Number - 121
Number of unique values in Startup Name - 121
Number of unique values in Industry - 11
Number of unique values in Business Description - 121
Number of unique values in Company Website - 112
Number of unique values in Number of Presenters - 5
Number of unique values in Male Presenters - 5
Number of unique values in Female Presenters - 3
Number of unique values in Couple Presenters - 2
Number of unique values in Pitchers Average Age - 3
Number of unique values in Started in - 12
Number of unique values in Pitchers City - 40
Number of unique values in Pitchers State - 20
Number of unique values in Yearly Revenue - 39
Number of unique values in Monthly Sales - 41
Number of unique values in Gross Margin - 22
Number of unique values in Original Ask Amount - 24
Number of unique values in Original Ask Equity - 19
Number of unique values in Valuation Requested - 50
Number of unique values in Received Offer - 2
Number of unique values in Accepted Offer - 2
Number of unique values in Total Deal Amount - 21
Number of unique values in Total Deal Equity - 27
Number of unique values in Total Deal Debt - 6
Number of unique values in Valuation Offered - 39
Number of unique values in Ashneer Investment Amount - 10
Number of unique values in Ashneer Investment Equity - 15
Number of unique values in Ashneer Debt Amount - 2
Number of unique values in Namita Investment Amount - 15
Number of unique values in Namita Investment Equity - 18
Number of unique values in Namita Debt Amount - 1
Number of unique values in Anupam Investment Amount - 13
Number of unique values in Anupam Investment Equity - 22
Number of unique values in Anupam Debt Amount - 1
Number of unique values in Vineeta Investment Amount - 10
Number of unique values in Vineeta Investment Equity - 13
Number of unique values in Vineeta Debt Amount - 1
Number of unique values in Aman Investment Amount - 15
Number of unique values in Aman Investment Equity - 20
Number of unique values in Aman Debt Amount - 1
Number of unique values in Peyush Investment Amount - 16
Number of unique values in Peyush Investment Equity - 19
Number of unique values in Peyush Debt Amount - 3
Number of unique values in Ghazal Investment Amount - 7
Number of unique values in Ghazal Investment Equity - 7
Number of unique values in Ghazal Debt Amount - 0
Number of unique values in Number of sharks in deal - 5
In [10]:
print(shark_tank['Season Number'].max(), "season \n")
print(shark_tank['Episode Number'].max(), "episodes \n")
print(shark_tank['Pitch Number'].max(), "startup companies came for pitching \n")
print(shark_tank['Episode Number'].value_counts().sort_values(ascending=True).unique(), "Pitches came per episodes")

1 season 

36 episodes 

121 startup companies came for pitching 

[3 4] Pitches came per episodes

Season 1 of Shark tank India was broadcasted on SonyLiv OTT platform.

Distribution of Cities from where Pitchers came¶

In [11]:
print(shark_tank['Pitchers City'].value_counts(), "\n")

plt.figure(figsize=(25,10))
plt.xticks(rotation=90)
plt.title("Shark Tank India")
ax = sns.countplot(x = "Pitchers City", data = shark_tank, order = shark_tank['Pitchers City'].value_counts().index)
for i in ax.containers:
    ax.bar_label(i,)

Mumbai                21
Delhi                 16
Bangalore             10
Pune                   9
Ahmedabad              6
Kolkata                5
Hyderabad              5
Gurgaon                3
Noida                  3
Surat                  2
Gandhinagar            2
Jaipur                 2
Jammu                  2
Nagpur                 2
Mangalore              1
Mathura                1
Varanasi               1
Bhagalpur              1
Lucknow                1
Coimbatore             1
Thane                  1
Pune, Delhi            1
Dehradun               1
Malegaon               1
Goa                    1
Ludhiana               1
Bangalore, Kolkata     1
Ernakulam              1
Panipat                1
Valsad                 1
Indore                 1
Chennai                1
Vadodara               1
Thiruvananthapuram     1
Nashik                 1
Darbhanga              1
Jalna                  1
Mohali,Delhi           1
Baramati               1
Modinagar              1
Name: Pitchers City, dtype: int64

Distribution of States from where Pitchers came¶

In [12]:
print(shark_tank['Pitchers State'].value_counts())

plt.figure(figsize=(25,10))
plt.xticks(rotation=45)
ax = sns.countplot(x = shark_tank['Pitchers State'], order = shark_tank['Pitchers State'].value_counts().index)
plt.title("Shark Tank India")
for i in ax.containers:
    ax.bar_label(i,)

Maharashtra               37
Delhi                     16
Gujarat                   13
Karnataka                 11
Uttar Pradesh              7
Telangana                  5
West Bengal                5
Haryana                    4
Jammu & Kashmir            2
Rajasthan                  2
Tamil Nadu                 2
Kerala                     2
Bihar                      2
Punjab                     2
Madhya Pradesh             1
Karnataka, West Bengal     1
Punjab, Delhi              1
Uttarakhand                1
Goa                        1
Maharashtra, Delhi         1
Name: Pitchers State, dtype: int64
In [13]:
print(shark_tank['Started in'].value_counts())

plt.figure(figsize=(20,10))
ax = sns.countplot(x = shark_tank['Started in'], order = shark_tank['Started in'].value_counts().index)
for i in ax.containers:
    ax.bar_label(i,)

2019.0    22
2020.0    14
2016.0    13
2021.0    12
2018.0    12
2017.0    11
2015.0     4
2014.0     3
2005.0     1
2013.0     1
2012.0     1
2022.0     1
Name: Started in, dtype: int64

Distribution of different industries that came for pitching¶

In [14]:
print(shark_tank['Industry'].value_counts())

tmp = shark_tank['Industry'].value_counts().sort_values(ascending=True)
fig = px.bar(tmp, x="Industry", title="<b> Shark Tank India </b>",color = "Industry", template='simple_white', text=tmp)
fig.show()

Food                   37
Beauty/Fashion         22
Manufacturing          17
Technology             11
Education               8
Services                7
Medical                 7
Electrical Vehicles     4
Animal/Pets             3
Hardware                3
Sports                  2
Name: Industry, dtype: int64

Gender wise distribution¶

In [15]:
print("Total pitchers -", int(shark_tank['Number of Presenters'].sum()), "\n")

print("Total Male pitchers -", int(shark_tank['Male Presenters'].sum()), "\n")

print("Total female pitchers -", int(shark_tank['Female Presenters'].sum()), "\n")

print("Male entrepreneurs percentage - ", round(shark_tank['Male Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100,2), "%\n", sep='')
print("Female entrepreneurs percentage - ", round(shark_tank['Female Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100,2), "%\n", sep='')
print("Couple entrepreneurs percentage - ", round(shark_tank.loc[shark_tank['Couple Presenters']==1]['Couple Presenters'].sum()/shark_tank['Number of Presenters'].sum()*100,2), "% (data incomplete)\n", sep='')

Total pitchers - 252 

Total Male pitchers - 177 

Total female pitchers - 75 

Male entrepreneurs percentage - 70.24%

Female entrepreneurs percentage - 29.76%

Couple entrepreneurs percentage - 9.92% (data incomplete)

Top 10 brands having Highest Yearly Revenue¶

In [16]:
print(shark_tank.groupby('Startup Name')['Yearly Revenue'].max().nlargest(10))

tmpdf = shark_tank.sort_values('Yearly Revenue', ascending=False)[0:10]
fig = px.bar(tmpdf, x="Startup Name", y='Yearly Revenue', color="Startup Name", title="Highest Revenue of the pitches", text=tmpdf['Yearly Revenue'].map(int).map(str) + "%")
fig.show()

Startup Name
French Crown          7200.0
Guardian Gears        2500.0
Raising Superstars    1300.0
PlayBoxTV             1020.0
Alpino                1000.0
Hammer Lifestyle      1000.0
Shades of Spring       900.0
Tagz Foods             700.0
Devnagri               500.0
Moonshine              372.0
Name: Yearly Revenue, dtype: float64

Top 10 brands having Highest Gross Margin¶

In [17]:
print(shark_tank.groupby('Startup Name')['Gross Margin'].max().nlargest(10))

tmpdf = shark_tank.sort_values('Gross Margin', ascending=False)[0:10]
fig = px.bar(tmpdf, x="Startup Name", y='Gross Margin', color="Startup Name", title="Highest Gross margin of the brands", text=tmpdf['Gross Margin'].map(int).map(str) + "%")
fig.show()

Startup Name
Poo-de-Cologne        150.0
Farda                 115.0
Cocofit                95.0
Auli                   80.0
Cos IQ                 75.0
Thea and Sid           75.0
Bummer                 70.0
French Crown           70.0
Moonshine              70.0
Nomad Food Project     70.0
Name: Gross Margin, dtype: float64

How many accepted or rejected Shark's Offers?¶

In [18]:
print(round(shark_tank['Received Offer'].value_counts(normalize=True)*100).astype(str).str.replace('.0', '%'), "\n")
sns.countplot(x='Received Offer', data=shark_tank, palette='deep')

1    73%
0    27%
Name: Received Offer, dtype: object
Out[18]:
<AxesSubplot:xlabel='Received Offer', ylabel='count'>

73% companies received investments while 27% startups could not get offers.

In [19]:
print(round(shark_tank['Accepted Offer'].value_counts(normalize=True)*100).astype(str).str.replace('.0', '%'), "\n")
sns.countplot(x='Accepted Offer', data=shark_tank, palette="viridis")

1.0    76%
0.0    24%
Name: Accepted Offer, dtype: object
Out[19]:
<AxesSubplot:xlabel='Accepted Offer', ylabel='count'>

76% Companies accepted investments they got and 24% startups did not accepted the offers.

Pitchers/Companies who rejected Shark's Offers¶

In [20]:
shark_tank.loc[shark_tank['Accepted Offer']==0, ["Startup Name","Original Ask Amount","Original Ask Equity","Valuation Requested","Valuation Offered"]]
Out[20]:
Startup Name Original Ask Amount Original Ask Equity Valuation Requested Valuation Offered
6 qZense Labs 100.0 0.25 40000 NaN
19 Torch-it 75.0 1.00 7500 NaN
20 La Kheer Deli 50.0 7.50 667 NaN
26 Kabira Handmad 100.0 5.00 2000 NaN
40 Morriko Pure Foods 100.0 3.00 3333 NaN
54 India Hemp and Co 50.0 4.00 1250 NaN
59 Keto India 150.0 1.25 12000 NaN
69 Moonshine 80.0 0.50 16000 NaN
70 Falhari 50.0 2.00 2500 NaN
72 Urban Monkey 100.0 1.00 10000 NaN
73 Guardian Gears 30.0 5.00 600 NaN
80 Alpino 150.0 2.00 7500 NaN
86 Aliste Technologies 60.0 5.00 1200 NaN
92 PDD Falcon 75.0 3.00 2500 NaN
93 PlayBoxTV 100.0 3.50 2857 NaN
103 ExperentialEtc 200.0 4.00 5000 NaN
105 C3 Med-Tech 35.0 6.00 583 NaN
112 Green Protein 60.0 2.00 3000 NaN
115 Woloo 50.0 4.00 1250 NaN
118 French Crown 150.0 0.33 45455 NaN
120 Devnagri 100.0 1.00 10000 NaN

Top 14 Investments¶

In [21]:
#Top 14 investments as per total deal/investment amount
print(shark_tank.groupby('Startup Name')['Total Deal Amount'].max().nlargest(14))

tmpdf = shark_tank.sort_values('Total Deal Amount', ascending=False)[0:14]
fig = px.bar(tmpdf, x="Startup Name", y='Total Deal Amount', color="Startup Name", title="Highest Investment as per deal amount (in lakhs)", text=tmpdf['Total Deal Amount'].map(int).map(str) + " lakhs")
fig.show()

Startup Name
Aas Vidyalaya          150.0
Annie                  105.0
Get-A-Whey             100.0
Hammer Lifestyle       100.0
Humpy A2               100.0
IN A CAN               100.0
Insurance Samadhan     100.0
Proxgy                 100.0
Raising Superstars     100.0
Revamp Moto            100.0
Skippi Ice Pops        100.0
Sunfox Technologies    100.0
The Renal Project      100.0
The Yarn Bazaar        100.0
Name: Total Deal Amount, dtype: float64

Which Shark invested the most and the least?¶

In [22]:
# Maximum Investment - as per Investment Amount
print("Aman invested -", max(shark_tank['Ashneer Investment Amount'].sum()/100, shark_tank['Namita Investment Amount'].sum()/100, shark_tank['Anupam Investment Amount'].sum()/100, shark_tank['Vineeta Investment Amount'].sum()/100,
    shark_tank['Aman Investment Amount'].sum()/100, shark_tank['Peyush Investment Amount'].sum()/100, shark_tank['Ghazal Investment Amount'].sum()/100), "crores")

Aman invested - 8.94490016 crores
In [23]:
# Minimum Investment - as per Investment Amount
print("Ghazal invested -", min(shark_tank['Ashneer Investment Amount'].sum()/100, shark_tank['Namita Investment Amount'].sum()/100, shark_tank['Anupam Investment Amount'].sum()/100, shark_tank['Vineeta Investment Amount'].sum()/100,
    shark_tank['Aman Investment Amount'].sum()/100, shark_tank['Peyush Investment Amount'].sum()/100, shark_tank['Ghazal Investment Amount'].sum()/100), "crores")

Ghazal invested - 1.299902525 crores

Industry wise investments by different Sharks¶

In [24]:
# Ashneer Grover's Investment
print(shark_tank[shark_tank['Ashneer Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
shark_tank[shark_tank['Ashneer Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Ashneer Investment Amount']>0] [["Startup Name","Ashneer Investment Amount","Ashneer Investment Equity"]].sort_values(by="Ashneer Investment Equity")
fig = px.bar(tmpdf, x="Ashneer Investment Equity", y='Ashneer Investment Amount', color="Startup Name", title="<b>Total equity received by Ashneer (in %) for investment (in lakhs)</b>", text=tmpdf['Ashneer Investment Amount'].map(int).map(str) + " lakhs")
fig.update_layout(dict(xaxis = dict(type="category")))
fig.show()

Food                   8
Electrical Vehicles    3
Education              3
Manufacturing          2
Beauty/Fashion         2
Animal/Pets            1
Sports                 1
Technology             1
Name: Industry, dtype: int64
In [25]:
# Namita Thapar's Investment
print(shark_tank[shark_tank['Namita Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
shark_tank[shark_tank['Namita Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Namita Investment Amount']>0] [["Startup Name","Namita Investment Amount","Namita Investment Equity"]].sort_values(by="Namita Investment Equity")
fig = px.bar(tmpdf, x="Namita Investment Equity", y='Namita Investment Amount', color="Startup Name", title="<b>Total equity received by Namita (in %) for investment (in lakhs)</b>", text=tmpdf['Namita Investment Amount'].map(int).map(str) + " lakhs")
fig.update_layout(dict(xaxis = dict(type="category")))
fig.show()

Beauty/Fashion    7
Food              6
Education         3
Medical           3
Manufacturing     2
Animal/Pets       1
Sports            1
Services          1
Name: Industry, dtype: int64
In [26]:
# Vineeta singh's Investments
print(shark_tank[shark_tank['Vineeta Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
shark_tank[shark_tank['Vineeta Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Vineeta Investment Amount']>0] [["Startup Name","Vineeta Investment Amount","Vineeta Investment Equity"]].sort_values(by="Vineeta Investment Equity")
fig = px.bar(tmpdf, x="Vineeta Investment Equity", y='Vineeta Investment Amount', color="Startup Name", title="<b>Total equity received by Vineeta (in %) for investment (in lakhs)</b>", text=tmpdf['Vineeta Investment Amount'].map(int).map(str) + " lakhs")
fig.update_layout(dict(xaxis = dict(type="category")))
fig.show()

Food                   8
Beauty/Fashion         4
Electrical Vehicles    1
Medical                1
Manufacturing          1
Sports                 1
Name: Industry, dtype: int64
In [27]:
# Peyush Bansal's Investments
print(shark_tank[shark_tank['Peyush Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
shark_tank[shark_tank['Peyush Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Peyush Investment Amount']>0] [["Startup Name","Peyush Investment Amount","Peyush Investment Equity"]].sort_values(by="Peyush Investment Equity")
fig = px.bar(tmpdf, x="Peyush Investment Equity", y='Peyush Investment Amount', color="Startup Name", title="<b>Total equity received by Peyush (in %) for investment (in lakhs)</b>", text=tmpdf['Peyush Investment Amount'].map(int).map(str) + " lakhs")
fig.update_layout(dict(xaxis = dict(type="category")))
fig.show()

Manufacturing     4
Beauty/Fashion    4
Food              4
Technology        4
Medical           3
Education         3
Hardware          2
Services          2
Animal/Pets       1
Sports            1
Name: Industry, dtype: int64
In [28]:
# Anupam Mittal's Investments
print(shark_tank[shark_tank['Anupam Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
shark_tank[shark_tank['Anupam Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Anupam Investment Amount']>0] [["Startup Name","Anupam Investment Amount","Anupam Investment Equity"]].sort_values(by="Anupam Investment Equity")
fig = px.bar(tmpdf, x="Anupam Investment Equity", y='Anupam Investment Amount', color="Startup Name", title="<b>Total equity received by Anupam (in %) for investment (in lakhs)</b>", text=tmpdf['Anupam Investment Amount'].map(int).map(str) + " lakhs")
fig.update_layout(dict(xaxis = dict(type="category")))
fig.show()

Beauty/Fashion         6
Food                   6
Manufacturing          4
Medical                3
Electrical Vehicles    1
Technology             1
Education              1
Animal/Pets            1
Sports                 1
Name: Industry, dtype: int64
In [29]:
# Aman Gupta's Investments
print(shark_tank[shark_tank['Aman Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
shark_tank[shark_tank['Aman Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Aman Investment Amount']>0] [["Startup Name","Aman Investment Amount","Aman Investment Equity"]].sort_values(by="Aman Investment Equity")
fig = px.bar(tmpdf, x="Aman Investment Equity", y='Aman Investment Amount', color="Startup Name", title="<b>Total equity received by Aman (in %) for investment (in lakhs)</b>", text=tmpdf['Aman Investment Amount'].map(int).map(str) + " lakhs")
fig.update_layout(dict(xaxis = dict(type="category")))
fig.show()

Food                   11
Beauty/Fashion          6
Manufacturing           4
Education               2
Technology              2
Medical                 2
Electrical Vehicles     1
Animal/Pets             1
Name: Industry, dtype: int64
In [30]:
# Ghazal Alagh's Investments
print(shark_tank[shark_tank['Ghazal Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False))
shark_tank[shark_tank['Ghazal Investment Amount']>0]['Industry'].value_counts().sort_values(ascending=False).plot.pie(autopct='%1.2f%%')
plt.show()

tmpdf = shark_tank.loc[shark_tank['Ghazal Investment Amount']>0] [["Startup Name","Ghazal Investment Amount","Ghazal Investment Equity"]].sort_values(by="Ghazal Investment Equity")
fig = px.bar(tmpdf, x="Ghazal Investment Equity", y='Ghazal Investment Amount', color="Startup Name", title="<b>Total equity received by Ghazal (in %) for investment (in lakhs)</b>", text=tmpdf['Ghazal Investment Amount'].map(int).map(str) + " lakhs")
fig.update_layout(dict(xaxis = dict(type="category")))
fig.show()

Food              3
Medical           2
Beauty/Fashion    1
Manufacturing     1
Name: Industry, dtype: int64

Number of Sharks in a Deal¶

In [31]:
print(round(shark_tank['Number of sharks in deal'].value_counts(normalize=True)*100).astype(str).str.replace('.0', '%'))
ax = sns.countplot(data = shark_tank, x = 'Number of sharks in deal')
print('The deals where more than or equals to 5 sharks are involved')
print(shark_tank.loc[shark_tank['Number of sharks in deal'] >= 5][["Startup Name","Total Deal Amount","Total Deal Equity"]])
for i in ax.containers:
    ax.bar_label(i,)

1.0    33%
2.0    31%
3.0    22%
4.0     7%
5.0     6%
Name: Number of sharks in deal, dtype: object
The deals where more than or equals to 5 sharks are involved
             Startup Name  Total Deal Amount  Total Deal Equity
15        Skippi Ice Pops              100.0               15.0
49  Find Your Kicks India               50.0               25.0
63               IN A CAN              100.0               10.0
79    Sunfox Technologies              100.0                6.0

Total amount asked by Pitchers¶

In [32]:
print(format_currency(shark_tank['Original Ask Amount'].sum()/100,'INR',locale='en_IN').replace('.00',''),'crores')

₹378.23 crores

Total Amount invested by the Sharks¶

In [33]:
print(format_currency(shark_tank['Total Deal Amount'].sum()/100,'INR',locale='en_IN').replace('.00',''),'crores')

₹39.03 crores
In [34]:
Amount = [shark_tank['Ashneer Investment Amount'].sum(), shark_tank['Namita Investment Amount'].sum(), shark_tank['Anupam Investment Amount'].sum(), shark_tank['Vineeta Investment Amount'].sum(),
    shark_tank['Aman Investment Amount'].sum(), shark_tank['Peyush Investment Amount'].sum(), shark_tank['Ghazal Investment Amount'].sum()]
name=['Ashneer','Namita','Anupam','Vineeta','Aman','Peyush','Ghazal']
df = {'Name':name, 'Total Amount':Amount }
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Amount'])
plt.xticks(fontsize=15)
plt.ylabel("Total Investment Amount (in lakhs)",fontsize=15)
for index,d in enumerate(Amount):
    plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Investment Amount by Sharks", fontsize=15)
plt.show()

Portfolios of different Sharks¶

In [35]:
# Peyush Bansal's Portfolio
print("Peyush Bansal's total investments", shark_tank[shark_tank['Peyush Investment Amount']>0][['Peyush Investment Amount']].count().to_string()[-2:])
print("Investment amount by Peyush", round(shark_tank['Peyush Investment Amount'].sum()/100, 2), "crores\n")
print("Equity received by Peyush", round(shark_tank['Peyush Investment Equity'].sum(), 2), "% in different companies\n")
print("Debt/loan amount by Peyush", round(shark_tank['Peyush Debt Amount'].sum()/100, 2), "crores\n")
print('-'*80)
print(shark_tank.loc[shark_tank['Peyush Investment Amount']>0][["Startup Name","Industry","Peyush Investment Amount"]].reset_index())

Peyush Bansal's total investments 28
Investment amount by Peyush 7.9 crores

Equity received by Peyush 317.84 % in different companies

Debt/loan amount by Peyush 1.17 crores

--------------------------------------------------------------------------------
    index           Startup Name        Industry  Peyush Investment Amount
0      22    Vivalyf Innovations         Medical                 28.000000
1      25                  Ariro   Manufacturing                 25.000000
2      27                Nuutjob  Beauty/Fashion                  8.330000
3      28               Meatyour            Food                 10.000000
4      29              EventBeep       Education                 10.000000
5      35                   LOKA      Technology                 13.330000
6      36                  Annie       Education                 35.000000
7      37             Carragreen   Manufacturing                 25.000000
8      38        The Yarn Bazaar   Manufacturing                 25.000000
9      43                    PNT      Technology                 25.000000
10     49  Find Your Kicks India  Beauty/Fashion                 10.000000
11     50          Aas Vidyalaya       Education                 50.000000
12     52             RoadBounce      Technology                 80.000000
13     58                WeSTOCK     Animal/Pets                 15.000000
14     61        The State Plate            Food                 40.000000
15     63               IN A CAN            Food                 20.000000
16     65          Sid07 Designs        Hardware                 25.000000
17     67         Hair Originals  Beauty/Fashion                 20.000000
18     76            KG Agrotech        Hardware                 10.000000
19     79    Sunfox Technologies         Medical                 20.000000
20     81        Isak Fragrances  Beauty/Fashion                 50.000000
21     85     Watt Technovations         Medical                  0.000253
22     87     Insurance Samadhan        Services                100.000000
23     88               Humpy A2            Food                 33.330000
24     90    Gold Safe Solutions   Manufacturing                 16.660000
25    108             Tweek Labs          Sports                 20.000000
26    109                 Proxgy      Technology                 50.000000
27    119         Store My Goods        Services                 25.000000
In [36]:
# Aman Gupta's Portfolio
print("Aman Gupta's total investments", shark_tank[shark_tank['Aman Investment Amount']>0][['Aman Investment Amount']].count().to_string()[-2:])
print("Investment amount by Aman", round(shark_tank['Aman Investment Amount'].sum()/100, 2), "crores\n")
print("Equity received by Aman", round(shark_tank['Aman Investment Equity'].sum(), 2), "% in different companies\n")
print("Debt/loan amount by Aman", round(shark_tank['Aman Debt Amount'].sum()/100, 2), "crores\n")
print('-'*80)
print(shark_tank.loc[shark_tank['Aman Investment Amount']>0][["Startup Name","Industry","Aman Investment Amount"]].reset_index())

Aman Gupta's total investments 29
Investment amount by Aman 8.94 crores

Equity received by Aman 164.25 % in different companies

Debt/loan amount by Aman 0.5 crores

--------------------------------------------------------------------------------
    index           Startup Name             Industry  Aman Investment Amount
0       0         BluePine Foods                 Food               25.000000
1       7              Peeschute       Beauty/Fashion               75.000000
2      11                 Bummer       Beauty/Fashion               37.500000
3      12            Revamp Moto  Electrical Vehicles               50.000000
4      15        Skippi Ice Pops                 Food               20.000000
5      18     Raising Superstars            Education               50.000000
6      21           Beyond Snack                 Food               25.000000
7      24                  Altor        Manufacturing               25.000000
8      25                  Ariro        Manufacturing               25.000000
9      27                Nuutjob       Beauty/Fashion                8.330000
10     28               Meatyour                 Food               10.000000
11     29              EventBeep            Education               10.000000
12     32                  Farda       Beauty/Fashion               15.000000
13     35                   LOKA           Technology               13.330000
14     38        The Yarn Bazaar        Manufacturing               25.000000
15     39      The Renal Project              Medical               50.000000
16     42       Hammer Lifestyle        Manufacturing              100.000000
17     44                Cocofit                 Food                0.000016
18     47           Beyond Water                 Food               37.500000
19     48              Let's Try                 Food               22.500000
20     49  Find Your Kicks India       Beauty/Fashion               10.000000
21     58                WeSTOCK          Animal/Pets               15.000000
22     63               IN A CAN                 Food               20.000000
23     64             Get-A-Whey                 Food               33.330000
24     71           Namhya Foods                 Food               50.000000
25    100               AyuRythm              Medical               75.000000
26    104             GrowFitter           Technology               50.000000
27    114          Jain Shikanji                 Food               10.000000
28    117               SneaKare       Beauty/Fashion                7.000000
In [37]:
# Ghazal Alagh's Portfolio
print("Ghazal Alagh's total investments", shark_tank[shark_tank['Ghazal Investment Amount']>0][['Ghazal Investment Amount']].count().to_string()[-2:])
print("Investment amount by Ghazal", round(shark_tank['Ghazal Investment Amount'].sum()/100, 2), "crores\n")
print("Equity received by Ghazal", round(shark_tank['Ghazal Investment Equity'].sum(), 2), "% in different companies\n")
print("Debt/loan amount by Ghazal", round(shark_tank['Ghazal Debt Amount'].sum()/100, 2), "crores\n")
print('-'*80)
print(shark_tank.loc[shark_tank['Ghazal Investment Amount']>0][["Startup Name","Industry","Ghazal Investment Amount"]])

Ghazal Alagh's total investments  7
Investment amount by Ghazal 1.3 crores

Equity received by Ghazal 46.7 % in different companies

Debt/loan amount by Ghazal 0.0 crores

--------------------------------------------------------------------------------
            Startup Name        Industry  Ghazal Investment Amount
75          The Sass Bar  Beauty/Fashion                 25.000000
79   Sunfox Technologies         Medical                 20.000000
85    Watt Technovations         Medical                  0.000253
88              Humpy A2            Food                 33.330000
90   Gold Safe Solutions   Manufacturing                 16.660000
91           Wakao Foods            Food                 25.000000
110   Nomad Food Project            Food                 10.000000
In [38]:
# Asgneer Grover's Portfolio
print("Ashneer Grover's total investments", shark_tank[shark_tank['Ashneer Investment Amount']>0][['Ashneer Investment Amount']].count().to_string()[-2:])
print("Investment amount by Ashneer", round(shark_tank['Ashneer Investment Amount'].sum()/100, 2), "crores\n")
print("Equity received by Ashneer", round(shark_tank['Ashneer Investment Equity'].sum(), 2), "% in different companies\n")
print("Debt/loan amount by Ashneer", round(shark_tank['Ashneer Debt Amount'].sum()/100, 2), "crores\n")
print('-'*80)
print(shark_tank.loc[shark_tank['Ashneer Investment Amount']>0][["Startup Name","Industry","Ashneer Investment Amount"]])

Ashneer Grover's total investments 21
Investment amount by Ashneer 5.39 crores

Equity received by Ashneer 93.24 % in different companies

Debt/loan amount by Ashneer 1.14 crores

--------------------------------------------------------------------------------
              Startup Name             Industry  Ashneer Investment Amount
0           BluePine Foods                 Food                      25.00
1            Booz Scooters  Electrical Vehicles                      20.00
3               Tagz Foods                 Food                      70.00
15         Skippi Ice Pops                 Food                      20.00
18      Raising Superstars            Education                      50.00
21            Beyond Snack                 Food                      25.00
23           Motion Breeze  Electrical Vehicles                      30.00
29               EventBeep            Education                      10.00
38         The Yarn Bazaar        Manufacturing                      25.00
45            Bamboo India        Manufacturing                      25.00
49   Find Your Kicks India       Beauty/Fashion                      10.00
50           Aas Vidyalaya            Education                      50.00
55                    Otua  Electrical Vehicles                       1.00
58                 WeSTOCK          Animal/Pets                      15.00
63                IN A CAN                 Food                      20.00
64              Get-A-Whey                 Food                      33.33
67          Hair Originals       Beauty/Fashion                      20.00
108             Tweek Labs               Sports                      20.00
109                 Proxgy           Technology                      50.00
110     Nomad Food Project                 Food                      10.00
114          Jain Shikanji                 Food                      10.00
In [39]:
# Nmita Thapar's Portfolio
print("Namita Thapar's Total Investments", shark_tank[shark_tank['Namita Investment Amount']>0][['Namita Investment Amount']].count().to_string()[-2:])
print("Investment amount by Namita", round(shark_tank['Namita Investment Amount'].sum()/100, 2), "crores\n")
print("Equity received by Namita", round(shark_tank['Namita Investment Equity'].sum(), 2), "% in different companies\n")
print("Debt/loan amount by Namita", round(shark_tank['Namita Debt Amount'].sum()/100, 2), "crores\n")
print('-'*80)
print(shark_tank.loc[shark_tank['Namita Investment Amount']>0][["Startup Name","Industry","Namita Investment Amount"]])

Namita Thapar's Total Investments 24
Investment amount by Namita 6.8 crores

Equity received by Namita 140.78 % in different companies

Debt/loan amount by Namita 0.25 crores

--------------------------------------------------------------------------------
              Startup Name        Industry  Namita Investment Amount
11                  Bummer  Beauty/Fashion                 37.500000
15         Skippi Ice Pops            Food                 20.000000
16            Menstrupedia       Education                 50.000000
24                   Altor   Manufacturing                 25.000000
27                 Nuutjob  Beauty/Fashion                  8.330000
32                   Farda  Beauty/Fashion                 15.000000
33                    Auli  Beauty/Fashion                 75.000000
36                   Annie       Education                 35.000000
39       The Renal Project         Medical                 50.000000
44                 Cocofit            Food                  0.000016
47            Beyond Water            Food                 37.500000
49   Find Your Kicks India  Beauty/Fashion                 10.000000
50           Aas Vidyalaya       Education                 50.000000
58                 WeSTOCK     Animal/Pets                 15.000000
63                IN A CAN            Food                 20.000000
79     Sunfox Technologies         Medical                 20.000000
83             Rare Planet   Manufacturing                 65.000000
85      Watt Technovations         Medical                  0.000253
91             Wakao Foods            Food                 25.000000
95            Kabaddi Adda          Sports                 40.000000
106    Colour Me Mad - CMM  Beauty/Fashion                 40.000000
110     Nomad Food Project            Food                 10.000000
117               SneaKare  Beauty/Fashion                  7.000000
119         Store My Goods        Services                 25.000000
In [40]:
# Anupam Mittal's Portfolio
print("Anupam Mittal's total investments", shark_tank[shark_tank['Anupam Investment Amount']>0][['Anupam Investment Amount']].count().to_string()[-2:])
print("Investment amount by Anupam", round(shark_tank['Anupam Investment Amount'].sum()/100, 2), "crores\n")
print("Equity received by Anupam", round(shark_tank['Anupam Investment Equity'].sum(), 2), "% in different companies\n")
print("Debt/loan amount by Anupam", round(shark_tank['Anupam Debt Amount'].sum()/100, 2), "crores\n")
print('-'*80)
print(shark_tank.loc[shark_tank['Anupam Investment Amount']>0][["Startup Name","Industry","Anupam Investment Amount"]])

Anupam Mittal's total investments 24
Investment amount by Anupam 5.34 crores

Equity received by Anupam 166.35 % in different companies

Debt/loan amount by Anupam 0.15 crores

--------------------------------------------------------------------------------
                 Startup Name             Industry  Anupam Investment Amount
2         Heart up my Sleeves       Beauty/Fashion                 12.500000
9                      Cos IQ       Beauty/Fashion                 25.000000
12                Revamp Moto  Electrical Vehicles                 50.000000
15            Skippi Ice Pops                 Food                 20.000000
22        Vivalyf Innovations              Medical                 28.000000
28                   Meatyour                 Food                 10.000000
31   ARRCOAT Surface Textures        Manufacturing                 50.000000
35                       LOKA           Technology                 13.330000
36                      Annie            Education                 35.000000
37                 Carragreen        Manufacturing                 25.000000
38            The Yarn Bazaar        Manufacturing                 25.000000
44                    Cocofit                 Food                  0.000016
45               Bamboo India        Manufacturing                 25.000000
48                  Let's Try                 Food                 22.500000
49      Find Your Kicks India       Beauty/Fashion                 10.000000
63                   IN A CAN                 Food                 20.000000
66           The Quirky Naari       Beauty/Fashion                 17.500000
67             Hair Originals       Beauty/Fashion                 20.000000
75               The Sass Bar       Beauty/Fashion                 25.000000
78                  PawsIndia          Animal/Pets                 50.000000
79        Sunfox Technologies              Medical                 20.000000
85         Watt Technovations              Medical                  0.000253
108                Tweek Labs               Sports                 20.000000
114             Jain Shikanji                 Food                 10.000000
In [41]:
# Vineeta Singh's Portfolio
print("Vineeta Singh's total investments", shark_tank[shark_tank['Vineeta Investment Amount']>0][['Vineeta Investment Amount']].count().to_string()[-2:])
print("Investment amount by Vineeta", round(shark_tank['Vineeta Investment Amount'].sum()/100, 2), "crores\n")
print("Equity received by Vineeta", round(shark_tank['Vineeta Investment Equity'].sum(), 2), "% in different companies\n")
print("Debt/loan amount by Vineeta", round(shark_tank['Vineeta Debt Amount'].sum()/100, 2), "crores\n")
print('-'*80)
print(shark_tank.loc[shark_tank['Vineeta Investment Amount']>0][["Startup Name","Industry","Vineeta Investment Amount"]])

Vineeta Singh's total investments 16
Investment amount by Vineeta 3.35 crores

Equity received by Vineeta 135.53 % in different companies

Debt/loan amount by Vineeta 0.3 crores

--------------------------------------------------------------------------------
            Startup Name             Industry  Vineeta Investment Amount
0         BluePine Foods                 Food                      25.00
1          Booz Scooters  Electrical Vehicles                      20.00
2    Heart up my Sleeves       Beauty/Fashion                      12.50
8                   NOCD                 Food                      20.00
9                 Cos IQ       Beauty/Fashion                      25.00
15       Skippi Ice Pops                 Food                      20.00
64            Get-A-Whey                 Food                      33.33
66      The Quirky Naari       Beauty/Fashion                      17.50
79   Sunfox Technologies              Medical                      20.00
88              Humpy A2                 Food                      33.33
90   Gold Safe Solutions        Manufacturing                      16.66
91           Wakao Foods                 Food                      25.00
95          Kabaddi Adda               Sports                      40.00
110   Nomad Food Project                 Food                      10.00
114        Jain Shikanji                 Food                      10.00
117             SneaKare       Beauty/Fashion                       7.00

Top Investments as per Equity Amount¶

In [42]:
print(shark_tank.groupby('Startup Name')['Total Deal Equity'].max().nlargest(15))
df1 = shark_tank.sort_values('Total Deal Equity', ascending=False)[0:15]
fig = px.bar(df1, x="Startup Name", y='Total Deal Equity', color="Startup Name", title="Highest Investment as per Equity percentage", text=df1['Total Deal Equity'].map(int).map(str)+'%')
fig.show()

Startup Name
Sid07 Designs            75.00
Booz Scooters            50.00
Isak Fragrances          50.00
Hammer Lifestyle         40.00
KG Agrotech              40.00
The Sass Bar             35.00
Vivalyf Innovations      33.33
Gold Safe Solutions      30.00
Heart up my Sleeves      30.00
Jain Shikanji            30.00
Colour Me Mad - CMM      25.00
Cos IQ                   25.00
Find Your Kicks India    25.00
PNT                      25.00
LOKA                     24.00
Name: Total Deal Equity, dtype: float64

Top Investments as per Loan Amount¶

In [43]:
print(shark_tank.groupby('Startup Name')['Total Deal Debt'].max().nlargest(15))
df1 = shark_tank.sort_values('Total Deal Debt', ascending=False)[0:15]
fig = px.bar(df1, x="Startup Name", y='Total Deal Debt', color="Startup Name", title="Highest Investment as per loan amount", text=df1['Total Deal Debt'])
fig.show()

Startup Name
Otua                        99.0
Namhya Foods                50.0
Store My Goods              50.0
Bamboo India                30.0
NOCD                        30.0
PNT                         25.0
The State Plate             25.0
Sid07 Designs               22.0
KG Agrotech                 20.0
ARRCOAT Surface Textures     NaN
Aas Vidyalaya                NaN
Agri tourism                 NaN
Aliste Technologies          NaN
Alpino                       NaN
Altor                        NaN
Name: Total Deal Debt, dtype: float64

Total Equity Received by Sharks¶

In [44]:
Equity = [shark_tank['Ashneer Investment Equity'].sum(), shark_tank['Namita Investment Equity'].sum(), shark_tank['Anupam Investment Equity'].sum(), shark_tank['Vineeta Investment Equity'].sum(),
    shark_tank['Aman Investment Equity'].sum(), shark_tank['Peyush Investment Equity'].sum(), shark_tank['Ghazal Investment Equity'].sum()]
df = {'Name':name, 'Total Equity':Equity }
plt.figure(figsize=(10,4))
plt.bar(df['Name'], df['Total Equity'])
plt.xticks(fontsize=15)
plt.ylabel("Total Equity (in %)",fontsize=14)
for index,d in enumerate(Equity):
    plt.text(x=index, y =d+2, s=f"{round(d,2)}", ha = 'center', fontdict=dict(fontsize=12))
plt.title("Total Equity received by Sharks", fontsize=15)
plt.show()

Companies who got same valuation as they requested¶

In [45]:
shark_tank.loc[shark_tank['Valuation Requested'] == shark_tank["Valuation Offered"]][["Startup Name","Valuation Requested","Valuation Offered"]]
Out[45]:
Startup Name Valuation Requested Valuation Offered
21 Beyond Snack 2000 2000.0
44 Cocofit 0 0.0
85 Watt Technovations 0 0.0

Some companies were on pre-revenue or didn't had any revenue (as of pitching day)¶

In [46]:
df2 = shark_tank.loc[shark_tank['Yearly Revenue'] == 0]
print(df2['Startup Name'])

23        Motion Breeze
41      Good Good Piggy
82     Julaa Automation
97             Scholify
99           Sabjikothi
113             On2Cook
Name: Startup Name, dtype: object
In [47]:
# Few companies got more amount than they asked/expected
shark_tank.loc[shark_tank['Original Ask Amount'] < shark_tank["Total Deal Amount"]][["Startup Name","Original Ask Amount","Total Deal Amount"]]
Out[47]:
Startup Name Original Ask Amount Total Deal Amount
0 BluePine Foods 50.0 75.0
15 Skippi Ice Pops 45.0 100.0
36 Annie 30.0 105.0
38 The Yarn Bazaar 50.0 100.0
42 Hammer Lifestyle 30.0 100.0
58 WeSTOCK 50.0 60.0
63 IN A CAN 50.0 100.0
75 The Sass Bar 40.0 50.0
88 Humpy A2 75.0 100.0
108 Tweek Labs 40.0 60.0
109 Proxgy 35.0 100.0
117 SneaKare 20.0 21.0
In [48]:
text = " Shark Tank India ".join(cat for cat in shark_tank['Startup Name'])
stop_words = list(STOPWORDS)
wordcloud = WordCloud(width=2000, height=800, stopwords=stop_words, background_color='black', colormap='Set2', collocations=False, random_state=2022).generate(text)
plt.figure(figsize=(25,20))
plt.imshow(wordcloud)
plt.axis("off")
plt.show()